LiDAR-based data assimilation during offshore transient events

Atmospheric conditions and instabilities affect directly the performance of modern large offshore wind farms and several offshore operations, particularly farther offshore in deep waters. However, our current knowledge regarding to the atmospheric processes over a wide range of spatiotemporal scales needs further improvements by the use of measurements, and sophisticated modelling of Marine Atmospheric Boundary Layer (MABL) processes relevant to the offshore wind energy. Processes like gravity waves, Open Convective Cells (OCCs), Low Level Jets (LLJs) affect both horizontal and vertical structures of MABL flow fields and the interactions between the ambient flow and offshore constructions. For example, LLJs are common physical processes over the Southern North Sea. These transient events occur during stably stratified atmosphere with jet cores at heights between 150 m and 300 m. Strong positive and negative shears are observed below and above the nose of LLJ (i.e a maxima in the vertical wind profile). Structure, timing, shape, and characteristics of LLJs influence the loads on turbines and the overall power generation of offshore wind parks. Therefore, precise modelling and measurement of these episodes are highly important.

While advanced measurement systems such as LiDAR provides important information on formation and characteristics of LLJs, such measurements are sparse in time and space. On the other hand, modelling tools are sensitive in prediction of LLJ characteristics such as LLJ’s height, spatial position, and timing, the choice of initial and boundary conditions, and planetary boundary layer schemes used in the Numerical Weather Prediction models (NWPs).  Predictive skills of these models can be enhanced through assimilation of available quality observational data with NWPs like Weather Research and Forecasting (WRF) model.

 

In this study, we use the WRF model to model wind variability for a geographical area covering the FINO1 offshore meteorological met-mast and Alpha Ventus offshore wind park (in the Southern North Sea). We first examine the performance of WRF, with an appropriate configuration, in forecasting few LLJ events. We then apply a LiDAR-based data assimilation (for sometimes during 2015) and study how different DA techniques (namely observational nudging and 3DVAR) can improve the accuracy of wind forecasting and reduce the model uncertainity during the LLJ events.